Qiongyan Wang
HKUST(GZ)
I'm Qiongyan Wang, a first-year Ph.D. student at the System Hub, affiliated with the Hong Kong University of Science and Technology (Guangzhou), under the supervision of Prof. Yuxuan Liang. I previously earned my Master's degree in Computer Science from the University of Copenhagen. My research interests mainly lie in Spatio-temporal (ST) data mining and AI for social good.
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Education
  • The Hong Kong University of Science and Technology (Guangzhou)
    The Hong Kong University of Science and Technology (Guangzhou)
    Ph.D. in Intelligence Transportation
    Sep. 2024 - Now
  • University of Copenhagen
    University of Copenhagen
    M.S. in Computer Science
    Sep. 2022 - Jul. 2024
  • Xidian University
    Xidian University
    B.S. in Software Engineering
    Sep. 2018 - Jul. 2022
Experience
  • Tencent
    Tencent
    Software Intern
    Aug. 2021 - Oct. 2021
Selected Publications (view all )
Spatio-Temporal Field Neural Networks for Air Quality Inference
Spatio-Temporal Field Neural Networks for Air Quality Inference

Yutong Feng, Qiongyan Wang, Yutong Xia, Junlin Huang, Siru Zhong, Yuxuan Liang

International Joint Conference on Artificial Intelligence (IJCAI) 2024 Conference

In this work, we make the first attempt to combine two different spatio-temporal perspectives, fields and graphs, by proposing a new model, Spatio-Temporal Field Neural Network, and its corresponding new framework, Pyramidal Inference.

[Paper]

Spatio-Temporal Field Neural Networks for Air Quality Inference
Spatio-Temporal Field Neural Networks for Air Quality Inference

Yutong Feng, Qiongyan Wang, Yutong Xia, Junlin Huang, Siru Zhong, Yuxuan Liang

International Joint Conference on Artificial Intelligence (IJCAI) 2024 Conference

In this work, we make the first attempt to combine two different spatio-temporal perspectives, fields and graphs, by proposing a new model, Spatio-Temporal Field Neural Network, and its corresponding new framework, Pyramidal Inference.

[Paper]

Deep Structural Knowledge Exploitation and Synergy for Estimating Node Importance Value on Heterogeneous Information Networks
Deep Structural Knowledge Exploitation and Synergy for Estimating Node Importance Value on Heterogeneous Information Networks

Yankai Chen, Yixiang Fang, Qiongyan Wang, Xin Cao, Irwin King

In Proceeding of AAAI Conference on Artificial Intelligence (AAAI) 2024 Conference

We propose a novel learning framework: SKES. Different from previous automatic learning designs, SKES exploits heterogeneous structural knowledge to enrich the informativeness of node representations.

[Paper]

Deep Structural Knowledge Exploitation and Synergy for Estimating Node Importance Value on Heterogeneous Information Networks
Deep Structural Knowledge Exploitation and Synergy for Estimating Node Importance Value on Heterogeneous Information Networks

Yankai Chen, Yixiang Fang, Qiongyan Wang, Xin Cao, Irwin King

In Proceeding of AAAI Conference on Artificial Intelligence (AAAI) 2024 Conference

We propose a novel learning framework: SKES. Different from previous automatic learning designs, SKES exploits heterogeneous structural knowledge to enrich the informativeness of node representations.

[Paper]

Fidelity Evaluation of Virtual Traffic Based on Anomalous Trajectory Detection
Fidelity Evaluation of Virtual Traffic Based on Anomalous Trajectory Detection

Chaoneng Li, Qianwen Chao, Guanwen Feng, Qiongyan Wang, Pengfei Liu, Yunan Li, Qiguang Miao

International Conference on Intelligent Robots and Systems (IROS) 2022 Conference

We design a perceptual evaluation on virtual traffic fidelity and derive a mapping from the reconstruction error to the evaluation score.

[Paper]

Fidelity Evaluation of Virtual Traffic Based on Anomalous Trajectory Detection
Fidelity Evaluation of Virtual Traffic Based on Anomalous Trajectory Detection

Chaoneng Li, Qianwen Chao, Guanwen Feng, Qiongyan Wang, Pengfei Liu, Yunan Li, Qiguang Miao

International Conference on Intelligent Robots and Systems (IROS) 2022 Conference

We design a perceptual evaluation on virtual traffic fidelity and derive a mapping from the reconstruction error to the evaluation score.

[Paper]

Velocity-based dynamic crowd simulation by data-driven optimization
Velocity-based dynamic crowd simulation by data-driven optimization

Pengfei Liu, Qianwen Chao, Henwei Huang, Qiongyan Wang, Zhongyuan Zhao, Qi Peng, Milo K. Yip, Elvis S. Liu, Xiaogang Jin

The Visual Computer 2022 Journal

We present a novel velocity-based framework based on data-driven optimization to build dynamic crowd simulation that allows interactive control of global navigation, local collision avoidance, and group formation.

[Paper]

Velocity-based dynamic crowd simulation by data-driven optimization
Velocity-based dynamic crowd simulation by data-driven optimization

Pengfei Liu, Qianwen Chao, Henwei Huang, Qiongyan Wang, Zhongyuan Zhao, Qi Peng, Milo K. Yip, Elvis S. Liu, Xiaogang Jin

The Visual Computer 2022 Journal

We present a novel velocity-based framework based on data-driven optimization to build dynamic crowd simulation that allows interactive control of global navigation, local collision avoidance, and group formation.

[Paper]

All publications